Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
Gradual trust and distrust in recommender systems
Fuzzy Sets and Systems
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
Modeling and evaluation of trust with an extension in semantic web
Web Semantics: Science, Services and Agents on the World Wide Web
Expert Systems with Applications: An International Journal
Exploring different types of trust propagation
iTrust'06 Proceedings of the 4th international conference on Trust Management
Practical aggregation operators for gradual trust and distrust
Fuzzy Sets and Systems
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Collaborative filtering based recommender system (CF-RS) provides personalized recommendations to users utilizing the experiences and opinions of their nearest neighbours. Although, collaborative filtering (CF) is the most successful and widely implemented filtering, data sparsity is still a major concern. In this work, we have proposed a fuzzy trust propagation scheme to alleviate the sparsity problem. Since trust is often a gradual trend, so trust to a person can be expressed more naturally using linguistic expressions. In this work, fuzzy trust is represented by linguistic terms rather than numerical values. We discuss the basic trust concepts such as fuzzy trust modeling, propagation and aggregation operators. An empirical evaluation of the proposed scheme on well known Movie-Lens dataset shows that fuzzy trust propagation allows reducing the sparsity problem of RSs while preserving the quality of recommendations.